Four Things Everyone Should Know to Improve Batch Normalization

9 June 2019

Papers citing "Four Things Everyone Should Know to Improve Batch Normalization"

11 / 11 papers shown

Title
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation Hyesu Lim Byeonggeun Kim Jaegul Choo Sungha Choi TTA 24 85 0 10 Feb 2023
A New Perspective for Understanding Generalization Gap of Deep Neural Networks Trained with Large Batch Sizes O. Oyedotun Konstantinos Papadopoulos Djamila Aouada AI4CE 24 11 0 21 Oct 2022
Understanding and Improving Group Normalization Agus Gunawan Xu Yin Kang Zhang 13 3 0 05 Jul 2022
Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks Bum Jun Kim Hyeyeon Choi Hyeonah Jang Dong Gu Lee Wonseok Jeong Sang Woo Kim 9 4 0 15 May 2022
LTD: Low Temperature Distillation for Robust Adversarial Training Erh-Chung Chen Che-Rung Lee AAML 19 26 0 03 Nov 2021
Batch Normalization Preconditioning for Neural Network Training Susanna Lange Kyle E. Helfrich Qiang Ye 22 9 0 02 Aug 2021
"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization Tianlong Chen Zhenyu (Allen) Zhang Xu Ouyang Zechun Liu Zhiqiang Shen Zhangyang Wang MQ 31 36 0 16 Apr 2021
High-Performance Large-Scale Image Recognition Without Normalization Andrew Brock Soham De Samuel L. Smith Karen Simonyan VLM 223 512 0 11 Feb 2021
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 219 1,399 0 04 Dec 2018
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 348 0 14 Jun 2018
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,326 0 05 Nov 2016